Computational Intelligence techniques are today widely used to solve complex engineering problems. Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems are nowadays adopted as hybrids techniques in the commercial and industrial environment. In this paper, we present an interesting application of the FUZZY-GA paradigm to Smart Grids. In particular, this study focuses on the possibility of tuning a Fuzzy Rule Base trying to discover, by means of a GA, a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making for the power flow management task in a microgrid. The RB optimization is obtained through Hierarchical Genetic Algorithm, based on an encoding scheme inspired by Nature, applied to the optimization of the FIS parameters. Tests show how the proposed controller scheme is effective in maximizing the economic return when dealing with the problem of power flows management in a microgrid, equipped with an energy storage system.
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